Building type classification using deep learning for transport planning - Khatua, Aniruddha; Goswami, Arkopal K.; Aithal, Bharath H. (TeMA - Journal of Land Use, Mobility and Environment, 2024)
The transportation and land use sectors are closely interdependent, and real-life circumstances often exhibit substantial reciprocal influences. Currently, efforts are being made to enhance transportation and land use sustainably; hence to achieve sustainability, it is necessary to have well-optimized plans and implementations for the advancements, which consequently leads to an increased demand for vast amounts of data. Conducting manual surveys to collect data on various types of buildings is considerably costly, requires much labor, and is time-consuming. Remote sensing technology has demonstrated significant promise to encompass a greater extent in a reduced timeframe, as well as to engage in thorough data collection and effectively manage and analyze the acquired data. This work centers on constructing a classification system that categorizes buildings depending on their use, specifically distinguishing between residential and non-residential structures. The classification challenge is accomplished through instance segmentation using the state-of-the-art YOLOV8 model architecture and remotely sensed images. The main objective of this project is to create base maps for travel analysis zones (TAZs) using identified buildings. To properly align the output images generated by the model, geographical data is appended to the output images derived from the original input images.